In the last decade, network representation learning (NRL) has become a common and important machine learning task for network-valued data. The goal of network representation learning is to identify low-dimensional representations of an observed network's vertices while preserving various aspects of the original graph like its topology, vertex attributes, or community structure [1]}, [2]}, [3]}. For a network with \(n\)  vertices, NRL seeks \(p\) -dimensional feature vectors with \(p < n\)  that capture representative structure of the original vertices and edges. These low-dimensional representations, called embeddings, are regularly used for routine machine learning tasks like clustering, classification, and prediction. NRL has provided important insights about relational systems in a variety of applications, including the study of social interactions [4]}, structural and functional connectivity of the brain [5]}, [6]}, and information dissemination on the internet [7]}.
